Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.
Journal
Computers, informatics, nursing : CIN
ISSN: 1538-9774
Titre abrégé: Comput Inform Nurs
Pays: United States
ID NLM: 101141667
Informations de publication
Date de publication:
22 Jan 2024
22 Jan 2024
Historique:
medline:
22
1
2024
pubmed:
22
1
2024
entrez:
22
1
2024
Statut:
aheadofprint
Résumé
This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.
Identifiants
pubmed: 38252546
doi: 10.1097/CIN.0000000000001100
pii: 00024665-990000000-00154
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
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